An Extended Nonlinear Primal-Dual Interior-Point Algorithm for Reactive-Power Optimization of Large-Scale Power Systems with Discrete Control Variables
An Extended Nonlinear Primal-Dual Interior-Point Algorithm for Reactive-Power Optimization of Large-Scale Power Systems with Discrete Control Variables
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DOI：
10.1109/mper.2002.4312572
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发表时间：
2002-09
期刊：
影响因子：
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通讯作者：
Mingbo Liu;Shiu Kit Tso;Ying Cheng
中科院分区：
文献类型：
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作者：
Mingbo Liu;Shiu Kit Tso;Ying Cheng
本文提出了一种新的离散变量和连续变量相结合的大规模电力系统无功优化算法。该算法通过在非线性原-对偶邻域点算法中引入罚函数，实现了优化过程中离散控制变量的逐次离散化。详细讨论了罚函数处理离散变量的原理、迭代过程中引入罚函数的时机以及罚因子的设置。为了在每次迭代中快速有效地求解高维线性校正方程，提出了一种新的数据结构重排方法。与现有的数据结构相比，它能有效地减少非零填充元素的数量，并且不会给三角分解带来困难。对14 ~ 538节点的测试系统的数值计算结果表明，该方法能给出近似最优解，具有良好的收敛性，适合于大规模系统的应用。
This paper presents a new algorithm for reactive-power optimization of large-scale power systems involving both discrete and continuous variables. This algorithm realizes successive discretization of the discrete control variables in the optimization process by incorporating a penalty function into the nonlinear primal-dual interior-point algorithm. The principle of handling these discrete variables by the penalty function, the timing of introducing the penalty function during iterations and the setting of penalty factors are discussed in detail. To solve the high-dimension linear correction equation speedily and efficiently in each iteration, a novel data-structure rearrangement is proposed. Compared with the existing data structures, it can effectively reduce the number of nonzero fill-in elements and does not give rise to difficulty in triangular factorization. The numerical results of test systems that range in size from 14 to 538 buses have shown that the proposed method can give near-optimum solutions, has good convergence, and is suitable for large-scale system applications.